The potential role of serum amyloid A as biomarker of rheumatic diseases: a systematic review and meta-analysis

The identification of novel, robust biomarkers for the diagnosis of rheumatic diseases (RDs) and the presence of active disease might facilitate early treatment and the achievement of favourable long-term outcomes. We conducted a systematic review and meta-analysis of studies investigating the acute phase reactant, serum amyloid A (SAA), in RD patients and healthy controls to appraise its potential as diagnostic biomarker. We searched PubMed, Scopus, and Web of Science from inception to 10 April 2024 for relevant studies. We evaluated the risk of bias and the certainty of evidence using the JBI Critical Appraisal Checklist and GRADE, respectively (PROSPERO registration number: CRD42024537418). In 32 studies selected for analysis, SAA concentrations were significantly higher in RD patients compared to controls (SMD = 1.61, 95% CI 1.24–1.98, p < 0.001) and in RD patients with active disease compared to those in remission (SMD = 2.17, 95% CI 1.21–3.13, p < 0.001). Summary receiving characteristics curve analysis showed a good diagnostic accuracy of SAA for the presence of RDs (area under the curve = 0.81, 95% CI 0.78–0.84). The effect size of the differences in SAA concentrations between RD patients and controls was significantly associated with sex, body mass index, type of RD, and study country. Pending the conduct of prospective studies in different types of RDs, the results of this systematic review and meta-analysis suggest that SAA is a promising biomarker for the diagnosis of RDs and active disease. Supplementary Information The online version contains supplementary material available at 10.1007/s10238-024-01413-0.

The typical dysregulation of inflammatory pathways in RDs, with consequent excess local and systemic inflammation, has led to the routine measurement of biomarkers of inflammation, e.g., C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), for initial assessment and monitoring, in combination with clinical evaluation and serological biomarkers specific for individual RDs [10][11][12].However, their limited diagnostic accuracy in observational studies has stimulated the search for novel, more robust biomarkers for diagnosing RDs and detecting changes in disease activity [13][14][15][16].
One potential candidate biomarker of RDs is represented by serum amyloid A (SAA) proteins.SAA proteins, primarily synthesized in the liver, are significantly activated during the acute phase response in the presence of inflammation [17].Circulating SAA concentrations can increase up to 1000-fold within the first 24-48 h of an acute phase response because of several stimulating factors, primarily pro-inflammatory cytokines [18,19].SAA can in turn activate the complement system, the nucleotide-binding domain leucine-rich repeat-containing family pyrin-domain containing 3 inflammasome, and several pro-inflammatory cytokines [20][21][22].Notably, in serum SAA is primarily bound to high density lipoprotein (HDL)-cholesterol, reducing the physiological anti-inflammatory effects of this lipoprotein [23].A number of studies have also reported that SAA is involved in cholesterol transport and recycling and exerts significant pro-atherogenic effects [24][25][26].Such effects may play a role in the complex interplay between dysregulated immunity, inflammation, and cardiovascular disease in RD patients [27,28].
Given the potential pathophysiological role of SAA in RDs, we conducted a systematic review and meta-analysis of studies investigating this acute phase reactant in patients with RDs and healthy controls and in RD patients with and without active disease.We speculated that higher SAA concentrations were significantly associated with the presence of RDs and active disease.Where possible, we also investigated associations between the effect size of the betweengroup differences and several study and patient characteristics, including lipid profile and conventional inflammatory biomarkers, and the diagnostic accuracy of the SAA.
Two investigators independently screened abstracts and full articles according to pre-defined inclusion and exclusion criteria.Inclusion criteria were: (i) the assessment of SAA concentrations, (ii) the comparison of patients with RDs and healthy controls in case-control studies, (iii) the inclusion of patients aged ≥ 18 years, (iv) the use of English language, and (v) the availability of the full-text of the article.Exclusion criteria were: (i) articles reporting duplicate or irrelevant data, (ii) the inclusion of participants under 18 years, and (iii) non-case-control studies.The investigators also hand searched the references of individual articles to identify additional studies.
The following variables were independently extracted for further analysis: year of publication, first author, country where the study was conducted, RD type and duration, sample size, age, male to female ratio, SAA concentrations, body mass index, C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), total, LDL-, and HDL-cholesterol, triglycerides, use of disease-modifying antirheumatic drugs or corticosteroids, area under the receiver operating characteristic curve (AUROC) with 95% confidence intervals (CIs), sensitivity, specificity, and cut-off values used for SAA.
The Joanna Briggs Institute Critical Appraisal Checklist for analytical studies was used to assess the risk of bias of individual studies [29].The Grades of Recommendation, Assessment, Development and Evaluation (GRADE) Working Group system were used to rank the certainty of evidence [30].The study adhered to the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) 2020 statement (Supplementary Table 1) [31], and was registered in an international repository (PROSPERO registration number: CRD42024537418).

Statistical analysis
We generated forest plots of standardized mean differences (SMDs) and 95% confidence intervals (CIs) to investigate differences in SAA concentrations between RD patients and healthy controls and between RD patients with active disease and those in remission (p < 0.05 for statistical significance).The Graph Data Extractor software was used to extract medians and interquartile ranges (San Diego, CA, USA).Established methods were used to extrapolate means and standard deviations from medians and interquartile ranges or ranges [32].Heterogeneity was assessed using the Q statistic (p < 0.10 for statistical significance) [33,34].Sensitivity analysis was conducted to investigate the stability of the results of the meta-analysis [35].Established methods were used to investigate the presence of publication bias [36][37][38].Univariate meta-regression and subgroup analyses were conducted to investigate associations between the effect size and year of publication, study country, RD type and duration, sample size, age, male to female ratio, body mass index, CRP, ESR, total, LDL-, and HDL-cholesterol, triglycerides, and use of DMARDs and corticosteroids.
The diagnostic accuracy of SAA was assessed by calculating the pooled sensitivity and specificity and generating a forest plot [39].Summary receiving characteristics (SROC) curve with 95% confidence region and prediction region was generated using the midas command in Stata [39].The relationship between prior probability, likelihood ratio, and posterior test probability was assessed by Fagan's nomogram plot [40].All analyses were performed using Stata 14 (Stata Corp., College Station, TX, USA).

Study selection
A flow chart describing the screening and selection process is presented in Fig. 1.We initially identified 2952 articles, of which 2902 were immediately excluded because they presented duplicate or irrelevant data.After a full-text review of the remaining 50 articles, a further ten were excluded because of missing information, six because participants were younger than 18 years, and two because they were not a case-control study.This led to the selection of 32 studies for further analysis  (Tables 1, 2 and 3).The initial level of certainty was ranked as low given the cross-sectional design of the selected studies.2).
The forest plot showed that the concentrations of SAA were overall significantly higher in RD patients when compared to controls (SMD = 1.61, 95% CI 1.24-1.98,p < 0.001; I 2 = 95.9%,p < 0.001; Fig. 2).Sensitivity analysis revealed that two studies showed a significant effect on the results of the meta-analysis of two studies [60,71] (the effect size ranged between 1.24 and 1.66, Fig. 3).This finding was further corroborated by funnel plot analysis, which revealed a marked distortive effect in the symmetry graph attributable to these studies (Fig. 4).Their removal led to a reduction in the pooled SMD which, however, remained significant (SMD = 1.06, 95% CI 0.85-1.28,p < 0.001, I 2 = 87.7%,p < 0.001).
The overall level of certainty was upgraded to moderate after considering the low-moderate risk of bias in all studies (no change), the high but partially explainable heterogeneity (no change), the lack of indirectness (no change), the large effect size (SMD = 1.61, upgrade one level) [73], and the presence of publication bias which was partially addressed using the "trim-and-fill" method (no change).

Diagnostic accuracy of serum amyloid A for the presence of rheumatic diseases
Five studies reported the ROC analysis of the diagnostic accuracy of SAA concentrations for RDs [57, 59, 65, 67, Fig. 5 Funnel plot of studies investigating the association between serum amyloid A concentrations and rheumatic diseases after "trimming and filling".Dummy studies and genuine studies are represented by enclosed circles and free circles, respectively Fig. 6 Bubble plot reporting the univariate meta-regression analysis between the effect size and male to female ratio (A) and body mass index (B) 68].A de novo ROC analysis was conducted using data from two additional studies [54,63].Sensitivity and specificity were extracted from these seven studies (nine comparator groups) which investigated a total of 910 participants (649 RD patients and 308 healthy controls, 68% females, mean age 41 years) (Table 2).Five studies were conducted in Asia [57,59,65,67,68], one in Europe [54], and one in Africa [63].Five comparator groups included individuals with RA [63,65,67], two with FMF [57,59], one with SSc [54], and one with AS [68].The risk of bias was assessed as low in five studies [57,59,63,65,68], and moderate in the remaining two [54,67] (Supplementary Table 2).
The risk of bias was assessed as low in all studies except one, which exhibited moderate risk [41] (Supplementary Table 2).
Assessment of publication bias, meta-regression, and subgroup analysis could not be conducted because of the insufficient number of studies.
The overall level of certainty was downgraded to very low after considering the low-moderate risk of bias in all Fig. 9 Forest plot of the pooled estimates of sensitivity and specificity of serum amyloid A concentrations for the presence of rheumatic diseases studies (no change), the high and unexplained heterogeneity (downgrade one level), the lack of indirectness (no change), the large effect size (SMD = 2.17, upgrade one level) [73], and the lack of assessment of publication bias (downgrade one level).

Discussion
This systematic review and meta-analysis has shown that SAA concentrations are significantly higher in patients with RDs when compared to healthy controls and in RD patients with active disease when compared to those in remission.In meta-regression and subgroup analysis, the effect size of the differences in SAA concentrations between RD patients and controls was not associated with and age, sample size, RD duration, CRP, ESR, total, HDL-, and LDL-cholesterol, and use of DMARDs or corticosteroids.By contrast, significant associations were observed with sex, body mass index, type of RD and study continent.In particular, there were significant differences vs. controls in studies of RA, SLE, FMF, TA and AS patients, but not in those in SSc or OA patients.Furthermore, studies conducted in Africa, unlike other continents, failed to report significant differences in SAA concentrations between RD patients and controls.
The lack of significant associations between the effect size and routinely used inflammatory biomarkers, i.e., CRP and ESR, suggests that the information provided by measuring SAA may complement, rather than duplicate, that provided by the CRP and ESR.Furthermore, the lack of association with disease duration suggests that the differences in SAA concentrations between RD patients and controls are likely to be manifest also in the early stages of the disease, potentially facilitating diagnosis and commencement of treatment.The reported associations between effect size and sex, indicating a relatively greater difference in SAA concentrations vs. controls in studies with a greater representation of male RD patients, represents an interesting finding as previous reports have shown similar SAA concentrations between males and females in healthy subjects [74,75], and in patients with cancer [76].Similarly, the significant and negative association observed between the effect size of the between-group differences in SAA concentrations and body mass index is at odds with previous reports which highlighted positive associations between SAA, body mass index, and obesity in non-RD populations [77,78].Future studies are required to confirm these findings and to investigate the pathophysiological and clinical significance of sex-related and body mass index-related differences in SAA Another important finding of our study was the good diagnostic performance of SAA concentrations for the overall presence of RDs, with pooled sensitivity, specificity, and AUC values of 0.72, 0.80, and 0.81, respectively.These figures compare favourably with those reported in studies investigating the diagnostic accuracy of CRP and ESR.For example, a prospective study assessing data from the Clinical Practice Research Datalink in UK primary care in 136,961 patients reported that the sensitivity and the specificity for any disease including infection, autoimmune  2) and 78.6 (96% CI 78.3-78.9)for ESR [13].Furthermore, the AUC value for autoimmune conditions was 0.71 (95% CI 0.70-0.72)for CRP and 0.71 (95% CI 0.69-0.72)for ESR whereas the AUC for combined CRP and ESR was marginally higher, 0.72 (95% CI 0.71-0.74),but still considerably below that observed for SAA in our study.The significant separation observed in Fagan's nomogram, with more than doubling of the post-vs.pre-test probability (54% vs. 25%) in patients with relatively high SAA concentrations and more than halving (10% vs. 25%) in those with relatively low SAA concentrations further supports the promising role of SAA as a biomarker of RDs.These results, however, need to be corroborated by appropriately designed prospective studies conducted in different types of RDs to investigate whether the SAA can significantly improve the diagnosis and management of this group and complement the information provided by current recommendations, i.e., clinical evaluation, imaging studies, inflammatory biomarkers, and specific serological markers.Another critical issue requiring study is whether SAA provides added diagnostic value when measured before, during, or after measuring conventional inflammatory biomarkers such as CRP and ESR.
Strengths of our systematic review and meta-analysis include the comprehensive assessment of SAA concentrations in different types of RDs, the evaluation of diagnostic accuracy, and the significant between-group differences observed in studies conducted in most continents, which supports the generalizability of our findings.One significant limitation was the relatively limited evidence available in patients with specific RDs, particularly SSc, OA, PMR, gout, HSP, PsA, and SpA.
In conclusion, the results of our systematic review and meta-analysis suggest that SAA is a promising biomarker for the overall diagnosis of RDs and the presence of active disease.Further prospective studies should investigate whether the diagnostic information provided by SAA significantly complements that provided by clinical evaluation, imaging studies, and available biomarkers, consequently enhancing the assessment and management of patients with RDs.

Fig. 2
Fig. 2 Forest plot of studies reporting serum amyloid A concentrations in patients with rheumatic diseases and healthy controls

Fig. 3 Fig. 4
Fig. 3 Sensitivity analysis of the association between serum amyloid A concentrations and rheumatic diseases

Fig. 7 Fig. 8
Fig. 7 Forest plot of studies reporting serum amyloid A concentrations in patients with rheumatic diseases and healthy controls according to disease type

Fig. 10 Fig. 11
Fig. 10 SROC curve with 95% confidence region and prediction region of serum amyloid A concentrations for the presence of rheumatic diseases

Fig. 12
Fig. 12 Forest plot of studies reporting serum amyloid A concentrations in patients with rheumatic diseases with active disease and patients in remission

Fig. 13
Fig. 13 Sensitivity analysis of the association between serum amyloid A concentrations and the presence of active disease

Table 2
Characteristics of the studies investigating the diagnostic accuracy of serum amyloid A concentrations for rheumatic diseases AS, ankylosing spondylitis; AUC , area under the curve; CI, confidence interval; FMF, familial Mediterranean fever; M/F, male to female ratio; NR, not reported; RA, rheumatoid arthritis; SSc, systemic sclerosis